STATISTICAL AND HYDROLOGICAL MODELING & THEIR ...epscor/pdfFiles/2016_racc_retreat/11...AND LAND...
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STATISTICAL AND HYDROLOGICAL MODELING & THEIR RELATION TO CLIMATE
AND LAND COVERIbrahim Mohammed
RACC and NEWRnet Retreat
February 6th 2016
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Statistical Modeling
■ Objective: describing and linking the variability in northeastern United States natural rivers with large atmospheric circulation patterns using wavelet analysis.
■ Natural Rivers Data: 72 USGS stream gages (HCDN-2009) ranging from the Susquehanna River in northern Pennsylvania to the Saint John River in Maine.
■ Climate Indices Data: the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the El-Niño 3.4 (ENSO 3.4), and the Pacific Decadal Oscillation (PDO).
A wavelet is a small wave that grows
and decays in a limited time-period.
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Correlation between climate indices and runoff regimes
the PDO and the
ENSO 3.4 climate
indices generally
show a positive
correlation with
Spring flows, while
the NAO and the AO
indices show a
negative correlation
with Spring flows at
many study sites
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Climate indices wavelet analysis
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Coupling between climate indices and runoff regimes
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Summary
■ A 2‒8 year strong periodicity activity is manifested by the
NAO, the AO, and the ENSO oscillations
■ The runoff regime variability for the northeastern United
States is connected to the NAO, the AO, and the ENSO 3.4
climate indices at low frequency (4‒8 years).
■ The correlation analysis for runoff regime and climate
indices winter conditions suggests some distinct spatial
patterns among most of the indices for both Spring and
Summer flows that are not coherent across the region but
rather exhibit trends with latitude or distance from the
coast.
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Hydrological ModelingMad River Example
■ Justin Guilbert is expanding
this modeling work to
examine the impacts of
climate on watershed
hydrology in the northeastern
United States
■ Cam White is also working
with the Mad River model to
gain understanding of
alternative approach to the
use of downscaled climate
data to project future
streamflow regimes.Mohammed, I. N., A. Bomblies, and B. C. Wemple (2015), The use of
CMIP5 data to simulate climate change impacts on flow regime within the
Lake Champlain Basin, J. Hydrol. Reg. St., 3, 160-186,
doi:10.1016/j.ejrh.2015.01.002.
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Hydrological ModelingMissisquoi River Example
■ This work has been used with
multiple studies within the
Integrated Assessment Model (IAM)
studies,
■ Work is on-going to understand the
spatial and temporal variability of
the streamflow nitrate release due
to land use and land cover changes
at the Lake Champlain Basin.